- A
Use AWS Glue ETL to apply one-hot encoding to merchant_category and min-max scaling to transaction_amount.
Why wrong: One-hot encoding 200 categories results in high dimensionality and sparse data, which is inefficient for many algorithms.
- B
Use Amazon EMR with Spark to apply ordinal encoding to merchant_category based on frequency, and log-transform the transaction_amount to reduce skewness.
Ordinal encoding handles high cardinality efficiently, and log transformation compresses extreme values, both reducing dimensionality and improving model performance.
- C
Use Amazon Athena to bin transaction_amount into 10 equal-width bins and replace merchant_category with its count encoding.
Why wrong: Binning loses granularity, and count encoding may not capture the category importance as well as frequency-based ordinal encoding.
- D
Use AWS Glue DataBrew to apply a one-hot encoding on merchant_category and a standard scaler on transaction_amount after removing outliers.
Why wrong: One-hot encoding is still problematic for 200 categories; removing outliers may discard valuable fraud examples. DataBrew is not the most efficient for 200 GB.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A financial services company is building a fraud detection model using historical transaction data stored in Amazon S3. The data includes features such as transaction amount, merchant category, time of day, and user location. The data scientist observes that the 'merchant_category' column is a text attribute with over 200 unique values. Additionally, the 'transaction_amount' column has a long-tail distribution with extreme outliers. The dataset is 200 GB in size, and the company wants to use Amazon SageMaker for model training. The data scientist needs to engineer features that capture the high-cardinality category and reduce the impact of outliers. What is the MOST efficient and effective approach to prepare this data?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use Amazon EMR with Spark to apply ordinal encoding to merchant_category based on frequency, and log-transform the transaction_amount to reduce skewness.
Option B is correct because ordinal encoding based on frequency handles high-cardinality categorical features efficiently without exploding dimensionality, and log-transform is a standard technique to reduce skewness in long-tail distributions. Using Amazon EMR with Spark provides distributed processing for the 200 GB dataset, making it scalable and cost-effective compared to single-node alternatives.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Glue ETL to apply one-hot encoding to merchant_category and min-max scaling to transaction_amount.
Why it's wrong here
One-hot encoding 200 categories results in high dimensionality and sparse data, which is inefficient for many algorithms.
- ✓
Use Amazon EMR with Spark to apply ordinal encoding to merchant_category based on frequency, and log-transform the transaction_amount to reduce skewness.
Why this is correct
Ordinal encoding handles high cardinality efficiently, and log transformation compresses extreme values, both reducing dimensionality and improving model performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Athena to bin transaction_amount into 10 equal-width bins and replace merchant_category with its count encoding.
Why it's wrong here
Binning loses granularity, and count encoding may not capture the category importance as well as frequency-based ordinal encoding.
- ✗
Use AWS Glue DataBrew to apply a one-hot encoding on merchant_category and a standard scaler on transaction_amount after removing outliers.
Why it's wrong here
One-hot encoding is still problematic for 200 categories; removing outliers may discard valuable fraud examples. DataBrew is not the most efficient for 200 GB.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to one-hot encoding for categorical data without considering cardinality, and assume scaling methods like min-max or standard scaling are always appropriate, ignoring the impact of outliers on these transformations.
Detailed technical explanation
How to think about this question
Ordinal encoding maps each category to an integer based on its frequency, which preserves the relative importance of categories and works well with tree-based models like XGBoost. Log-transform compresses the dynamic range of skewed features, making the distribution more Gaussian-like and improving model convergence; it is particularly effective for financial transaction amounts where extreme values are common but not necessarily anomalous.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon EMR with Spark to apply ordinal encoding to merchant_category based on frequency, and log-transform the transaction_amount to reduce skewness. — Option B is correct because ordinal encoding based on frequency handles high-cardinality categorical features efficiently without exploding dimensionality, and log-transform is a standard technique to reduce skewness in long-tail distributions. Using Amazon EMR with Spark provides distributed processing for the 200 GB dataset, making it scalable and cost-effective compared to single-node alternatives.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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